Analysis of the Mismatch between Tanzania Household Budget Survey and National Panel Survey Data in Poverty and Inequality Levels and Trends
Bibliographic record
Abstract
This study carries out a thorough \n investigation of the potential sources of mismatch in \n poverty and inequality levels and trends between the \n Tanzania National Panel Survey and Household Budget Survey. \n The main findings of the study include the following. First, \n the difference in poverty levels between the Household \n Budget Survey and the National Panel Survey is essentially \n explained by the differences in the methods of estimating \n the poverty line. Second, the discrepancy in poverty trends \n can be mainly attributed to the difference in inter-year \n temporal price deflators, and, to a lesser extent, spatial \n price deflators. The use of the consumer price index for \n adjusting consumption variation across years would show a \n decline in poverty during the past five years for the \n Household Budget Survey and the National Panel Survey. \n Third, despite noticeable differences in the methods of \n household consumption data collection, the Household Budget \n Survey and National Panel Survey show close mean household \n consumption levels in the last rounds, when using the \n consumer price index to adjust for inter-year price \n variations. Mean household consumption levels in the \n Household Budget Survey 2011/12 and National Panel Survey \n 2010/11 are comparable, and the mean consumption level in \n the National Panel Survey 2012/13 is around 10 percent \n higher. The difference is driven by higher levels of \n aggregate and food consumption by the better-off groups in \n the National Panel Survey. Fourth, the mismatch in \n inequality trends and pro-poor growth patterns between the \n two surveys could not be resolved and is a subject for \n further analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".